I'm very familiar with correlation networks but I can't seem to grasp my head around how Bayesian Networks are constructed.
How are the "edges" determined? How is the structure determined? I was speaking to somebody the other day who gave a lecture on Bayesian networks and he mentioned they are built one-node at a time instead of fully-connected correlation networks that are essentially created from a symmetric pairwise correlation matrix.
I'm experimenting with the concept using the iris dataset just to understand how these methods can be approached.
How are nodes connected in a Bayesian network? Can they be connected to categorical nodes as well (e.g. species from
Has anyone seen any implementations in Python with NetworkX that could help me understand how these could be created from real data?
R could be useful too but I'm less familiar with